基于贝叶斯正则化的TDBPNN模型在中国外贸预报中的应用及评估

Abstract

Abstract: Based on nonlinear prediction ideas of reconstructing phase space,this paper presents a time delay BP neural network model,whose generalization is improved utilizing Bayes’ regularization.Furthermore the model is applied to forecast the import and export trades in China.The results show that the improved TDBPNN model has excellent generalization capabilities,which can not only learn the historical curve,but efficiently predict the trend of trade development.In contrast to conventional evaluation of forecasts,we assess the model by calculating the nonlinear characteristics of the predicted and original time series besides analyzing the precision of forecasting.The estimated values demonstrate that the dynamics of the system producing the original series has been reasonably captured in this model.

Key words: nonlinear prediction, import and export trades, phase space reconstruction, BP neural networks, Bayes’ regularization

CLC Number:

F201
C931

ZHU Shu-jin, LAI Ming-yong. An Application of the TDBPNN Model Based on Bayes’ Regularization to Forecasting China’s Foreign Trade and Evaluation[J]. Chinese Journal of Management Science, 2005, (1): 1-8.

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An Application of the TDBPNN Model Based on Bayes’ Regularization to Forecasting China’s Foreign Trade and Evaluation

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